孪生网络pytorch代码示例

孪生网络是一种用于处理成对数据的神经网络结构，常用于分类任务。下面是一个使用PyTorch实现的孪生网络的代码示例[^2]：

import torch
import torch.nn as nn

class SiameseNetwork(nn.Module):
    def __init__(self):
        super(SiameseNetwork, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=10),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2),
            nn.Conv2(64, 128, kernel_size=7),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2),
            nn.Conv2(128, 128, kernel_size=4),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2),
            nn.Conv2d(128, 256, kernel_size=4),
            nn.ReLU(inplace=True),
        )
        self.fc = nn.Sequential(
            nn.Linear(256 * 6 * 6, 4096),
            nn.Sigmoid()
        )

    def forward_once(self, x):
        output = self.conv(x)
        output = output.view(output.size()[0], -1)
        output = self.fc(output)
        return output

    def forward(self, input1, input2):
        output1 = self.forward_once(input1)
        output2 = self.forward_once(input2)
        return output1, output2

# 创建模型实例
model = SiameseNetwork()

这段代码定义了一个名为`SiameseNetwork`的孪生网络模型，它包含了卷积层和全连接层。在`forward`方法中，输入数据会分别通过两个相同的网络结构，然后将两个输出进行比较或合并，用于分类任务。